#!/bin/bash

#当前路径,不需要修改
cur_path=`pwd`

export PYTHONWARNINGS='ignore:semaphore_tracker:UserWarning'

#集合通信参数,不需要修改
#保证rank table file 文件rank_table_8p.json存放在和test同级的configs目录下
export JOB_ID=9999001
export RANK_SIZE=1
export ENABLE_RUNTIME_V2=1
#export RANK_TABLE_FILE=${cur_path}/../configs/rank_table_8p.json
RANK_ID_START=0


# 数据集路径,保持为空,不需要修改
data_path=""

#设置默认日志级别,不需要修改
export ASCEND_GLOBAL_LOG_LEVEL=3

#基础参数 需要模型审视修改
#网络名称，同目录名称
Network="MaskRcnn_ID0011_for_TensorFlow"

batch_size=2
total_steps=20

#TF2.X独有，不需要修改
#export NPU_LOOP_SIZE=${train_steps}

#维测参数，precision_mode需要模型审视修改
precision_mode="allow_mix_precision"
#维持参数，以下不需要修改
over_dump=False
data_dump_flag=False
data_dump_step="10"
profiling=False
autotune=False

# 帮助信息，不需要修改
if [[ $1 == --help || $1 == -h ]];then
    echo"usage:./train_full_8p.sh <args>"
    echo " "
    echo "parameter explain:
    --precision_mode           precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision)
    --over_dump		           if or not over detection, default is False
    --data_dump_flag		   data dump flag, default is 0
    --data_dump_step		   data dump step, default is 10
    --profiling		           if or not profiling for performance debug, default is False
    --autotune                 whether to enable autotune, default is False
    --data_path		           source data of training
    -h/--help		           show help message
    "
    exit 1
fi

#参数校验，不需要修改
for para in $*
do
    if [[ $para == --precision_mode* ]];then
        precision_mode=`echo ${para#*=}`
    elif [[ $para == --over_dump* ]];then
        over_dump=`echo ${para#*=}`
        over_dump_path=${cur_path}/output/overflow_dump
        mkdir -p ${over_dump_path}
    elif [[ $para == --data_dump_flag* ]];then
        data_dump_flag=`echo ${para#*=}`
        data_dump_path=${cur_path}/output/data_dump
        mkdir -p ${data_dump_path}
    elif [[ $para == --data_dump_step* ]];then
        data_dump_step=`echo ${para#*=}`
    elif [[ $para == --profiling* ]];then
        profiling=`echo ${para#*=}`
        profiling_dump_path=${cur_path}/output/profiling
        mkdir -p ${profiling_dump_path}
    elif [[ $para == --autotune* ]];then
        autotune=`echo ${para#*=}`
        mv $install_path/fwkacllib/data/rl/Ascend910/custom $install_path/fwkacllib/data/rl/Ascend910/custom_bak
        mv $install_path/fwkacllib/data/tiling/Ascend910/custom $install_path/fwkacllib/data/tiling/Ascend910/custom_bak
        autotune_dump_path=${cur_path}/output/autotune_dump
        mkdir -p ${autotune_dump_path}/GA
        mkdir -p ${autotune_dump_path}/rl
        cp -rf $install_path/fwkacllib/data/tiling/Ascend910/custom ${autotune_dump_path}/GA/
        cp -rf $install_path/fwkacllib/data/rl/Ascend910/custom ${autotune_dump_path}/RL/
    elif [[ $para == --data_path* ]];then
        data_path=`echo ${para#*=}`
    elif [[ $para == --bind_core* ]]; then
        bind_core=`echo ${para#*=}`
        name_bind="_bindcore"
    fi
done

#校验是否传入data_path,不需要修改
if [[ $data_path == "" ]];then
    echo "[Error] para \"data_path\" must be confing"
    exit 1
fi

#autotune时，先开启autotune执行单P训练，不需要修改
if [[ $autotune == True ]]; then
    train_full_1p.sh --autotune=$autotune --data_path=$data_path
    wait
    autotune=False
fi

#修改save ckpt,print
sed -i "s|save_checkpoints_steps=90000|save_checkpoints_steps=${total_steps}|g" $cur_path/../distributed_executer.py
sed -i "s|log_step_count_steps=100|log_step_count_steps=1|g" $cur_path/../distributed_executer.py

#训练开始时间，不需要修改
start_time=$(date +%s)

#进入训练脚本目录，需要模型审视修改
cd $cur_path/../
for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
do
    #设置环境变量，不需要修改
    echo "Device ID: $RANK_ID"
    export RANK_ID=$RANK_ID
	
	# 自行添加环境变量

	export DEVICE_ID=$RANK_ID
	DEVICE_INDEX=$DEVICE_ID
    export DEVICE_INDEX=${DEVICE_INDEX}
	export FUSION_TENSOR_SIZE=1000000000
    # for producible results
    export TF_DETERMINISTIC_OPS=1
    export TF_CUDNN_DETERMINISM=1
    
    #创建DeviceID输出目录，不需要修改
    if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then
        rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID}
        mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt
    else
        mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt
    fi
    
    

    #执行训练脚本，以下传参不需要修改，其他需要模型审视修改
    #--data_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path
    corenum=`cat /proc/cpuinfo |grep 'processor' |wc -l`
    let a=RANK_ID*${corenum}/8
    let b=RANK_ID+1
    let c=b*${corenum}/8-1
    if [ "x${bind_core}" != x ];then
        bind_core="taskset -c $a-$c"
    fi
    ${bind_core} python3 mask_rcnn_main.py --mode=train \
        --rank=$RANK_ID \
        --total_steps=$total_steps \
        --Data_path=$data_path \
        --train_batch_size=2 \
        --training_file_pattern=${data_path}/train* \
        --validation_file_pattern=${data_path}/val* \
        --val_json_file=${data_path}/instances_val2017.json \
        --eval_batch_size=2 \
        --model_dir=result_npu\
        --over_dump=${over_dump} \
        --over_dump_path=${over_dump_path} \
        > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
        #--data_dump_flag=${data_dump_flag} \
        #--data_dump_step=${data_dump_step} \
        #--data_dump_path=${data_dump_path} \
        #--profiling=${profiling} \
        #--profiling_dump_path=${profiling_dump_path} \
        #--autotune=${autotune} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
done 
wait

#训练结束时间，不需要修改
end_time=$(date +%s)
e2e_time=$(( $end_time - $start_time ))

#参数回改
sed -i "s|save_checkpoints_steps=${total_steps}|save_checkpoints_steps=90000|g" $cur_path/../distributed_executer.py
sed -i "s|log_step_count_steps=1|log_step_count_steps=100|g" $cur_path/../distributed_executer.py

#结果打印，不需要修改
echo "------------------ Final result ------------------"
#输出性能FPS，需要模型审视修改
FPSper=`grep "] global_step/sec:" $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk '{print $6}'|tail -n +3|awk '{sum+=$1} END {print  sum/NR}'`
FPS=`awk 'BEGIN{printf "%f\n",'${batch_size}'*'${RANK_SIZE}'*'${FPSper}'}'`
#打印，不需要修改
echo "Final Performance images/sec : $FPS"

#输出CompileTime
CompileTime=`grep "INFO:tensorflow:loss" $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log |head -n 2|awk '{if (NR>1) print $7}' | awk -F '(' '{print $2}'`

#输出训练精度,需要模型审视修改
train_accuracy=`grep "Average Precision" $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|head -1|awk '{print $13}'`
#打印，不需要修改
echo "Final Train Accuracy : ${train_accuracy}"
echo "E2E Training Duration sec : $e2e_time"

#稳定性精度看护结果汇总
#训练用例信息，不需要修改
BatchSize=${batch_size}
DeviceType=`uname -m`
CaseName=${Network}${name_bind}_bs${BatchSize}_${RANK_SIZE}'p_RT2_perf'

##获取性能数据
#吞吐量，不需要修改
ActualFPS=${FPS}
#单迭代训练时长，不需要修改
TrainingTime=`awk 'BEGIN{printf "%.2f\n",1/'${FPSper}'}'`

#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
grep "] loss =" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk '{print $7}'|cut -d , -f 1 > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt

#最后一个迭代loss值，不需要修改
ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`

#关键信息打印到${CaseName}.log中，不需要修改
echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
#echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "CompileTime = ${CompileTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
